8 citations · 10 across the 7 of their papers we have counts for
9 papers
Learning to Route in Visual Space via Multi-Step Embedding Retrieval
Tianyu Chen, Mingyuan Zhou, Jiaxing Wu
LLM agents rely on retrieval tools to access external knowledge, yet visual agentic search remains severely bottlenecked by standard single-step retrievers. In current pipelines, t…
Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
Madhuri Shanbhogue, Zhe Li, Shanfeng Zhang +86
We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage t…
Contrastive Weak-to-strong Generalization
Houcheng Jiang, Junfeng Fang, Jiaxin Wu +5
Weak-to-strong generalization provides a promising paradigm for scaling large language models (LLMs) by training stronger models on samples from aligned weaker ones, without requir…
Evolution of Concepts in Language Model Pre-Training
Xuyang Ge, Wentao Shu, Jiaxing Wu +3
Language models obtain extensive capabilities through pre-training. However, the pre-training process remains a black box. In this work, we track linear interpretable feature evolu…
Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward
Yanming Wan, Jiaxing Wu, Marwa Abdulhai +2
Effective conversational agents like large language models (LLMs) must personalize their interactions to adapt to user preferences, personalities, and attributes across diverse dom…
Deliberation in Latent Space via Differentiable Cache Augmentation
Luyang Liu, Jonas Pfeiffer, Jiaxing Wu +2
Techniques enabling large language models (LLMs) to "think more" by generating and attending to intermediate reasoning steps have shown promise in solving complex problems. However…
RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs
Jiaxing Wu, Lin Ning, Luyang Liu +7
LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users' behavior from their past activities. However, their effectiveness often hinges on th…
Uncertainty Estimation of Large Language Models in Medical Question Answering
Jiaxin Wu, Yizhou Yu, Hong-Yu Zhou
Large Language Models (LLMs) show promise for natural language generation in healthcare, but risk hallucinating factually incorrect information. Deploying LLMs for medical question…
User-LLM: Efficient LLM Contextualization with User Embeddings
Lin Ning, Luyang Liu, Jiaxing Wu +6
Large language models (LLMs) have achieved remarkable success across various domains, but effectively incorporating complex and potentially noisy user timeline data into LLMs remai…